Method for predicting convergence disorders caused by concussion or other neuropathology

ABSTRACT

A method for predicting abnormal eye convergence in a human or animal subject may involve tracking eye movement of at least one eye of the subject to generate eye tracking data for the subject and using the eye tracking data to predict whether the subject has abnormal eye convergence. A method for diagnosing a brain injury in a human or animal subject may involve tracking eye movement of at least one eye of the subject to generate eye tracking data for the subject, using the eye tracking data to predict whether the subject has abnormal eye convergence, and predicting whether a brain injury has occurred in the subject, based on the prediction of whether the subject has abnormal eye convergence.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit under 35 U.S.C. § 119 U.S.Provisional Application No. 62/255,011, filed Nov. 13, 2015. Theforegoing application is hereby incorporated herein by reference in itsentirety for all purposes.

INCORPORATION BY REFERENCE

All patent applications, patents and other publications referenced inthis application are hereby incorporated by reference in their entirety.For example, Patent Cooperation Treaty Application No.PCT/US2013/033672, filed Mar. 25, 2013, and U.S. Provisional PatentApplication No. 61/881,014, filed Sep. 23, 2013, are both herebyincorporated by reference in their entireties.

FIELD OF THE INVENTION

The present invention relates to medical diagnostic methods. Morespecifically, the invention relates to a method for predicting,assessing and/or quantifying convergence disorders caused by concussionor other neuropathology.

BACKGROUND

Traumatic brain injury (TBI) is a very serious and significant healthproblem. Every year, at least 1.7 million TBIs occur in the UnitedStates, and they are a contributing factor in about one third of allinjury-related deaths. The incidence of TBI, as measured by combinedemergency department (ED) visits, hospitalizations, and deaths, rosesteadily from 2001 to 2010. For example, from 2001 to 2010, TBI ratesincreased from 521 to 824 per 100,000 population. Between 3.2 and 5.3million people (1.1%-1.7% of the U.S. population) live with long-termdisabilities that result from TBI. These are likely underestimates ofthe prevalence of TBI, because they do not include persons with TBIsequelae who were treated and released from EDs, those who sought carein other health-care settings, and those who did not seek treatment.

Traumatic brain injuries can be very challenging to diagnose and treat.One of the primary challenges posed by TBI is the heterogeneous natureof such injury, in terms of etiology, anatomic sequelae, and physiologicand psychologic impact. The etiology of injury affects the anatomicsequelae and ranges from global mechanisms, such asacceleration/deceleration and blast, to potentially more focalmechanisms, such as blunt impact and penetrating trauma. Some injurymechanisms result in structural changes to the brain that can bevisualized using conventional imaging, such as MRI and CT scan, whileother injuries appear radiographically normal.

Concussion is probably the best known form of TBI and is the most commonform of civilian radiographically normal brain injury. Concussion mostoften results from blunt impact, and it is typically not detectable byconventional radiographic imaging, such as computed tomography (CT)scan. Concussion is defined in part by transient loss or disruption ofneurologic function. The term “subconcussion” is used to describe thesequelae of brain injury in the absence of transient loss or disruptionof neurologic function. For the purposes of the present application, theterms “concussion,” “subconcussion” and “blast injury” may sometimes bereferred to generally as “non-structural brain injury.”

Blast injury resembles blunt impact brain injury, in that both may beassociated with radiographically apparent cerebral edema andintracranial hemorrhage. Like concussion, blast injury is veryfrequently radiographically normal, yet mild or moderate blast injury isstrongly associated with post-traumatic stress disorder and othercognitive dysfunctions. Blunt impact and penetrating trauma can resultin both diffuse and focal injury. One mechanism by which focal braininjury leads to neurologic damage is cortical spreading depression,which is currently only thought measurable using invasive means.

Brain injury may be associated with short term sequelae, includingheadaches and memory problems, and longer term problems, includingdementia, Parkinsonism and motor-neuron disease. Both concussion andmild blast injury may be associated with post-traumatic stress disorderand cognitive impairment. Clinical tests for concussion are not veryreliable, and thus concussion remains a diagnosis that is difficult totreat, because it is difficult to detect.

Many cases of trauma result in elevated intracranial pressure. Ifuntreated, acute elevations in intracranial pressure (ICP) due to braininjury can result in permanent neurologic impairment or death.

One method of diagnosing and pinpointing TBI is eye movement tracking.The clinical basis for eye tracking as a diagnostic for brain injury hasancient roots. 3500 years ago, Greek physicians wrote a surgicaltreatise stating that eyes that are askew may be evidence of braininjury. Prior to the invention of radiographic imaging, the assessmentof eye movements was a major modality of diagnosis of neurologicimpairment, with entire textbooks dedicated to this topic. Modern eraoptometrists can detect abnormal eye movements in up to 90% of patientswith so-called “mild” traumatic brain injury or concussion.

The most commonly detected abnormal eye movement associated with braininjury is a vergence problem. Vergence is the ability of the both eyesto focus together on a single point. If the point moves closer to thenose, the pupils converge. Following the point in space—or whilewatching TV—requires sustained vergence. Previous studies using eyetracking to assess patients with post-concussive symptoms suggest thatthese deficits may persist beyond the acute phase of injury.

Traumatic brain injury can impact eye movement through a multitude ofmechanisms, including direct compression of cranial nerves, trauma tocranial nerves, injury to cranial nerve nuclei and supranuclear impacts.In eye movement tracking, an eye tracker device is used to measuremovements of the eyes, and the movements are used to assess brainfunction. Spatial calibration of the eye tracker device is oftenperformed for each individual being tracked. With calibration, theeye-tracker measures the relative position of pupil and cornealreflection for a period of about 400-800 ms, while the subject looks ata target or targets of known position, to generate meaningful spatialcoordinates during subsequent pupil movement. One problem with spatialcalibration, however, is that the process assumes relatively preservedneurologic function, because it requires the subject to follow commandsand look at specific points.

It is conceivable that the process of spatial calibration may maskdeficits in ocular motility. If there is a persistent and replicableweakness in movement of an eye, the camera will interpret the eye'sability to move in the direction of that weakness as the full potentialrange of motion in that direction, due to the calibration process. Inother words, if the subject is directed to look at a position butconsistently only moves halfway there, the calibration process willaccount for that when tracking subsequent eye movements and interpretmovements to the halfway point as occurring at the full range of normalmotion. If, during calibration, one eye only makes it half-way to thetarget, but the other eye is fully there, the camera will interpret botheyes as being together when one performs half the eye movement as theother. Thus, binocular spatial calibration may preclude detection ofdisconjugate gaze, unless each eye is calibrated separately using adichoptic apparatus.

Conjugate gaze is the motion of both eyes in the same direction at thesame time. Disconjugate gaze, or strabismus, is a failure of the eyes toturn together in the same direction. Normal coordinated movements of theeyes produces conjugate gaze, in which the eyes are aligned forbinocular 3-dimensional vision. Misalignment results in loss of thisvision. With the visual axis of each eye fixated on a different point,diplopia (or double vision) usually results and may be perceived as ablurred image if the two images are very closely aligned. However, ifthe image from the weaker eye is suppressed by higher cortical centers,there is only one image with loss of visual acuity (or a blurred image).

Assessment of eye movement conjugacy is commonly performed by primarycare physicians, neurologists, ophthalmologists, neurosurgeons,emergency medicine doctors, and trauma surgeons, to rapidly assessglobal neurologic functioning. When various eye movement tests areperformed, in conjunction with the remainder of the neurophthalmic andphysical evaluation, one can localize neurologic lesions and quantitateocular motility deficits with great accuracy. Despite this capability,however, these tests are not used routinely in the emergency setting,due to the need for a trained practitioner to administer them, therequirement for sophisticated equipment, and the urgent nature of manyneurologic disorders.

Progress has been made in methods and kits for using eye tracking toassess brain injury. For example, U.S. Provisional Application No.61/881,014, filed Sep. 23, 2013, the disclosure of which is hereinincorporated by reference in its entirety, teaches methods for trackingeye movement, and methods and kits for assessing conjugacy anddisconjugacy of gaze and strabismus. Despite this progress, however, itwould be advantageous to have further improvements in methods, systemsand kits for assessing brain injury using eye tracking. Ideally, suchmethods, systems and kits could be used not only for assessing anddiagnosing concussion, but could also be used for assessing other TBIand/or other neuropathology.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a graph showing the receiver operating curve in a balancedmodel for pediatric concussion.

FIG. 2 is a scatterplot showing the convergence blurry and break(double) have a very smooth relationship. Spearman correlation of 0.752(p-value<0.001)

FIG. 3 is a scatterplot showing the relationship of Model predictions toactual values of convergence blurry spearman correlation of 0.785(p-value<0.001).

FIG. 4 is a diagram showing the impact of variance on aspect ratio.

FIG. 5 is a table listing metrics in a logistic regression model thatcorrelated eye tracking metrics to the state of being concussed.

FIG. 6 is a table of gender for the validation population.

FIG. 7: is a bar graph showing prevalence of different symptoms inconcussed patients

FIG. 8 is a bar graph showing prevalence of different signs in patientswith concussion

FIG. 9 is a receiver operating curve for eyetracking as a method ofdiagnosing concussion in the balanced sample of 32 patients and 32control subjects. The area under the curve was 0.854.

FIG. 10 is a scatter plot showing the predicted probability ofconcussion for cases and controls in the balanced sample. Concussedpatients are identified by blue rhombus while controls are identified bygreen circles. The solid black line indicates the model cutoff, abovewhich model will classify subject as concussed.

FIG. 11 is a receiver operating curve for eyetracking as a method ofdiagnosing concussion in a cross-validation sample of 24 patients and 51control subjects. The area under the curve was 0.789.

FIG. 12 is a scatter plot showing the predicted values for cases andcontrols in the cross-validation sample. Concussed patients areidentified by blue rhombus while controls are identified by greencircles. The solid black line indicates the model cutoff, above whichmodel will classify subject as concussed.

FIG. 13 is a receiver operating curve for eyetracking as it correlatesto abnormality in near point of convergence (as defined by NPC>6 cm) in32 concussed subjects. The area under the curve was 0.81.

BRIEF SUMMARY

In one aspect, a method for predicting abnormal eye convergence in ahuman or animal subject may involve tracking eye movement of at leastone eye of the subject to generate eye tracking data for the subject andusing the eye tracking data to predict whether the subject has abnormaleye convergence. In some embodiments, tracking eye movement may involvetracking movement of both eyes of the subject. In some embodiments,generating the eye tracking data may involve analyzing the tracked eyemovement and comparing the tracked eye movement to a normal or mean eyemovement. In some embodiments, generating the eye tracking data mayfurther involve, after the comparing step, calculating a standarddeviation or p value for the tracked eye movement as compared to thenormal or mean eye movement. Comparing the tracked eye movement mayinvolve comparing eye movement of one eye of the subject to eye movementof the other eye of the subject. Alternatively, comparing the trackedeye movement may involve comparing eye movement of both eyes of thesubject to eye movement of one or both eyes of one or more othersubjects or controls.

In some embodiments, the method may also involve predicting whether abrain injury has occurred in the subject, based on the prediction ofwhether the subject has abnormal eye convergence. For example,predicting whether the brain injury has occurred may involve predictingwhether a concussion has occurred. In some embodiments, the eye movementis tracked for at least 40 seconds.

In another aspect, a method for diagnosing a brain injury in a human oranimal subject may involve: tracking eye movement of at least one eye ofthe subject to generate eye tracking data for the subject; using the eyetracking data to predict whether the subject has abnormal eyeconvergence; and predicting whether a brain injury has occurred in thesubject, based on the prediction of whether the subject has abnormal eyeconvergence. In some embodiments, predicting whether the brain injuryhas occurred involves predicting whether a concussion has occurred.

In another aspect, a method for measuring, assessing and/or quantifyingabnormal eye convergence in a human or animal subject may involvetracking eye movement of at least one eye of the subject to generate eyetracking data for the subject and using the eye tracking data tomeasure, assess and/or quantify eye convergence of the subject.

In another aspect, a method for measuring, assessing and/or quantifyinga level of brain injury in a human or animal subject may involvetracking eye movement of at least one eye of the subject to generate eyetracking data for the subject, using the eye tracking data to predictwhether the subject has abnormal eye convergence, and measuring,assessing and/or quantifying the level of brain injury in the subject,based on the prediction of whether the subject has abnormal eyeconvergence.

In another aspect, a method for measuring, assessing and/or quantifyingbrain injury in a human or animal subject may involve: tracking eyemovement of at least one eye of the subject; collecting raw x and ycartesian coordinates of pupil position; normalizing the raw x and ycartesian coordinates; calculating one or more individual metrics; andmeasuring, assessing and/or quantifying brain injury in the subject,based at least in part on the calculated metrics. In some embodiments,the brain injury is concussion.

In another aspect, a system for predicting abnormal eye convergence in ahuman or animal subject may include a device for tracking eye movementand a processor integrated into or coupled with the device forprocessing the tracked eye movement to generate eye tracking data andpredicting whether the subject has abnormal eye convergence based on theeye tracking data. The device may be any suitable eye tracking device,such as any eye tracking device described in this application, any othercurrently available eye tracking device, a webcam device, goggles, orthe like.

In another aspect, a non-transitory computer-readable medium may haveinstructions stored thereon for predicting abnormal eye convergence in ahuman or animal subject, the instructions configured to perform thefollowing steps: receiving eye movement data pertaining to eye movementof one or both eyes of the subject; analyzing the eye movement data ofone or both eyes of the subject; comparing eye movement data of one orboth eyes of the subject to a normal or mean eye movement; andpredicting whether the subject has abnormal eye convergence, based onthe comparison of the eye movement data. In some embodiments, theinstructions may be further configured to perform the step ofcalculating a standard deviation or p value for eye movement of one orboth eyes of the subject as compared to the normal or mean eye movement,before the predicting step. Some embodiments may further includeinstructions stored thereon for measuring, assessing and/or quantifyingbrain injury in a human or animal subject, the instructions furtherconfigured to perform the following steps: tracking eye movement of atleast one eye of the subject; collecting raw x and y cartesiancoordinates of pupil position; normalizing the raw x and y cartesiancoordinates; and calculating one or more individual metrics.

In another aspect, a method for assessing or quantitating structural andnon-structural traumatic brain injury may involve: tracking eye movementof at least one eye of the subject; analyzing eye movement of at leastone eye of the subject; comparing eye movement of at least one eye ofthe subject to a normal or mean eye movement; and, optionally,calculating a standard deviation or p value for eye movement of at leastone eye of the subject as compared to the normal or mean eye movement.

In some instances, eye movement of both eyes of the subject are trackedand analyzed. In some instances, both x and y coordinates of eyeposition for one or both eyes of a subject are collected for at leastabout 100, 500, 1,000, 5,000, 10,000, 50,000, 100,000, 200,000 or moreeye positions. In some instances, the eye position is effectively thepupil position. In some instances the eye movement is tracked for about30, 60, 90, 100, 120, 150, 180, 200, 220, 240, 270, 300, 360 or moreseconds.

The comparing eye movement of at least one eye of the subject to anormal or mean eye movement may feature comparing eye movement of atleast one eye of the subject to the eye movement of the other eye of thesubject or may feature comparing eye movement of at least one eye of thesubject to the eye movement of an eye of one or more other subjects orcontrols. In some instances, the comparing eye movement of at least oneeye of the subject to a normal or mean eye movement may featurecomparing the eye movement of both eyes of the subject to the eyemovement of one or both eyes of one or more other subjects or controls.

In some instances, the method may feature collecting raw x and ycartesian coordinates of pupil position, normalizing the raw x and yCartesian coordinates, and sorting the data by eye.

The method may also feature calculating individual metrics, such as, forinstance, segment mean, segment median, and segment variance. The methodmay also feature calculating specific metrics such as, for example,L. varYtop=Var( y ₁,average k=1:5,1)  (13)R. varYtop=Var( y ₂,average k=1:5,1)  (14)L. varXrit=Var( x ₁,average k=1:5,2)  (15)R. varXrit=Var( x ₂,average k=1:5,2)  (16)L. varYbot=Var( y ₁,average k=1:5,3)  (17)R. varYbot=Var( y ₂,average k=1:5,3)  (18)L. varXlef=Var( x ₁,average k=1:5,4)  (19)L. varXlef=Var( x ₂,average k=1:5,4)  (20)L. varTotal=Average(Var( x ₁,average k=1:5)+Var( y ₁,averagek=1:5))  (21)R. varTotal=Average(Var( x ₂,average k=1:5)+Var( y ₂,averagek=1:5))  (22)or segment standard deviation and segment skew such as, for instance,L. SkewTop=Skew( y ₁,average k=1:5,1)  (27)R. SkewTop=Skew( y ₂,average k=1:5,1)  (28)L. SkewRit=Skew( x ₁,average k=1:5,2)  (29)R. SkewRit=Skew( x ₂,average k=1:5,2)  (30)L. SkewBot=Skew(y ₁,average k=1:5,3)  (31)R. SkewBot=Skew(y ₂,average k=1:5,3)  (32)L. SkewLef=Skew( x ₁,average k=1:5,4)  (33)R. SkewLef=Skew( x ₂,average k=1:5,4)  (34)or segment normalized skew, such as, for instance,

$\begin{matrix}{{{{SkewNorm}\left( {\overset{\_}{x}}_{j,{k\; \cdot \; 1}} \right)} = \frac{{Skew}\mspace{11mu}\left( {\overset{\_}{x}}_{j,{k\; \cdot \; 1}} \right)}{\sigma_{{\overset{\_}{x}}_{j,{k\mspace{11mu} \cdot \; 1}}}}},} & (35) \\{{{{SkewNorm}\left( {\overset{\_}{y}}_{j,{k\; \cdot \; 1}} \right)} = \frac{{Skew}\mspace{11mu}\left( y_{j,{k\; \cdot \; 1}} \right)}{\sigma_{y_{j,{k\; \cdot \; 1}}}}},} & (36)\end{matrix}$L. SkewTopNorm=SkewNorm( y ₁,average k=1:5,1)  (37)R. SkewTopNorm=SkewNorm( y ₂,average k=1:5,1)  (38)L. SkewRitNorm=SkewNorm( x ₁,average k=1:5,2)  (39)R. SkewRitNorm=SkewNorm( x ₂,average k=1:5,2)  (40)L. SkewBotNorm=SkewNorm(y ₁,average k=1:5,3)  (41)R. SkewBotNorm=SkewNorm(y ₂,average k=1:5,3)  (42)L. SkewLefNorm=SkewNorm( x ₁,average k=1:5,4)  (43)R. SkewLefNorm=SkewNorm( x ₂,average k=1:5,4)  (44)

The method may also feature calculating box height, box width, box area,or box aspect ratio.

Box HeightBoxHeight_(j,k) =y _(j,k,1) −y _(j,k,3)  (45)Box WidthBoxWidth_(j,k) =x _(j,k,2) −x _(j,k,4)  (46)Box Aspect Ratio

$\begin{matrix}{{AspectRatio}_{j,k} = \frac{{BoxHeight}_{j,k}}{{BoxWidth}_{j\; \cdot \; k}}} & (47)\end{matrix}$Box AreaBoxArea_(j,k)=BoxHeight_(j,k)xBoxWidth_(j,k)  (48)

The method may also feature calculating conjugacy of eye movement orvariance from perfect conjugacy of eye movement, such as, for example,

$\begin{matrix}{{{{Conj}\mspace{14mu}{var}\; X\;{top}} = \frac{{\sum\left( {\hat{x}}_{1} \right)^{2}} - 0}{\sum{\hat{x}}_{1}}},} & (57) \\{{{{Conj}\mspace{14mu}{var}\; X\;{rit}} = \frac{{\sum\left( {\hat{x}}_{2} \right)^{2}} - 0}{\sum{\hat{x}}_{2}}},} & (58) \\{{{{Conj}\mspace{14mu}{var}\; X\;{bot}} = \frac{{\sum\left( {\hat{x}}_{3} \right)^{2}} - 0}{\sum{\hat{x}}_{3}}},} & (59) \\{{{{Conj}\mspace{14mu}{var}\; X\;{lef}} = \frac{{\sum\left( {\hat{x}}_{4} \right)^{2}} - 0}{\sum{\hat{x}}_{4}}},} & (60) \\{{{{Conj}\mspace{14mu}{var}\; Y\;{top}} = \frac{{\sum\left( {\hat{y}}_{1} \right)^{2}} - 0}{\sum{\hat{y}}_{1}}},} & (61) \\{{{{Conj}\mspace{14mu}{var}\; Y\;{rit}} = \frac{{\sum\left( {\hat{y}}_{2} \right)^{2}} - 0}{\sum{\hat{y}}_{2}}},} & (62) \\{{{{Conj}\mspace{14mu}{var}\; Y\;{bot}} = \frac{{\sum\left( {\hat{y}}_{3} \right)^{2}} - 0}{\sum{\hat{y}}_{3}}},} & (63) \\{{{{Conj}\mspace{14mu}{var}\; Y\;{rit}} = \frac{{\sum\left( {\hat{y}}_{4} \right)^{2}} - 0}{\sum{\hat{y}}_{4}}},} & (64) \\{{{{Conj}\mspace{14mu}{Corr}\;{XY}\;{top}} = \frac{\sum{{\hat{x}}_{1}{\hat{y}}_{1}}}{{\sum{\hat{x}}_{1}} - 1}},} & (65) \\{{{{Conj}\mspace{14mu}{Corr}\;{XY}\;{rit}} = \frac{\sum{{\hat{x}}_{2}{\hat{y}}_{2}}}{{\sum{\hat{x}}_{2}} - 1}},} & (66) \\{{{{Conj}\mspace{14mu}{Corr}\;{XY}\;{bot}} = \frac{\sum{{\hat{x}}_{3}{\hat{y}}_{3}}}{{\sum{\hat{x}}_{3}} - 1}},} & (67) \\{{{{Conj}\mspace{14mu}{Corr}\;{XY}\;{lef}} = \frac{\sum{{\hat{x}}_{4}{\hat{y}}_{4}}}{{\sum{\hat{x}}_{4}} - 1}},} & (68)\end{matrix}$or variance x ratio top/bottom (conjugacy), variance y ratio top/bottom(conjugacy), variance x ratio left/right (conjugacy), or variance yratio left/right (conjugacy).

In some instances, one or more of the L height, L width, L area, LvarXrit, L varXlef, L varTotal, R height, R width, R area, R varYtop, RvarXrit, R varXlef, R varTotal, Conj varX, Conj varXrit, Conj varXbot,Conj varXlef and Conj varYlef may be especially useful for demonstratingor detecting or assessing structural or non-structural traumatic braininjury such as, for instance, a concussion or blast injury.

In addition combining one of more of the above metrics using techniquesincluding but not limited to “best subset”, “LASSO”, “random forest” or“logistic regression” may result in increased sensitivity of the eyetracking relative to use of only a single metric.

A standard deviation or p value of 0.25, 0.3, 0.4, 0.5, 0.75, 0.8, 0.9,1.0, 1.5, 2.0, 2.5 or more may reflect that a subject has structural ornon-structural traumatic brain injury such as, for instance, aconcussion, subconcussion or blast injury. As such, the methodsdescribed herein may be used to detect concussion, subconcussion andblast injury and assess or determine the severity of the same.

In another aspect, a method for diagnosing a disease characterized by orfeaturing structural and non-structural traumatic brain injury in asubject may involve: tracking eye movement of at least one eye of thesubject; analyzing eye movement of at least one eye of the subject;comparing eye movement of at least one eye of the subject to a normal ormean eye movement; and, optionally calculating a standard deviation or pvalue for eye movement of at least one eye of the subject.

In another aspect, methods for assessing or quantitating structural andnon-structural traumatic brain injury or diagnosing a diseasecharacterized by or featuring structural and non-structural traumaticbrain injury in a subject may involve: tracking eye movement of at leastone eye of the subject; collecting raw x and y cartesian coordinates ofpupil position; normalizing the raw x and y Cartesian coordinates; andcalculating one or more individual metric.

In some instances, eye movement of both eyes of the subject are trackedand analyzed. In some instances, both x and y coordinates of eyeposition for one or both eyes of a subject are collected for at leastabout 100, 500, 1,000, 5,000, 10,000, 50,000, 100,000, 200,000 or moreeye positions. In instances where the eye movements of both eyes aretracked, the method may additionally feature sorting the data by eye.

In another aspect, a kit useful for detecting, screening for orquantitating structural and non-structural traumatic brain injury in asubject may include a device for tracking eye movement, one or moremeans for analyzing eye movement tracking data such as, for instance, analgorithm or computer program, and instructions. Processing eye movementobservations, making measurements of eye movement observations,determining distributions of values measured and performing statisticaltests may all be accomplished using suitable computer software that maybe included in such a kit.

In another aspect, a computer system or computing device can be used toimplement a device that includes the processor and the display, the eyemovement/gaze tracker component, etc. The computing system includes abus or other communication component for communicating information and aprocessor or processing circuit coupled to the bus for processinginformation. The computing system can also include one or moreprocessors or processing circuits coupled to the bus for processinginformation. The computing system also includes main memory, such as arandom access memory (RAM) or other dynamic storage device, coupled tothe bus for storing information, and instructions to be executed by theprocessor. Main memory can also be used for storing positioninformation, temporary variables, or other intermediate informationduring execution of instructions by the processor. The computing systemmay further include a read only memory (ROM) or other static storagedevice coupled to the bus for storing static information andinstructions for the processor. A storage device, such as a solid statedevice, magnetic disk or optical disk, is coupled to the bus forpersistently storing information and instructions.

The computing system may be coupled via the bus to a display, such as aliquid crystal display, or active matrix display, for displayinginformation to a user. An input device, such as a keyboard includingalphanumeric and other keys, may be coupled to the bus for communicatinginformation and command selections to the processor. In anotherimplementation, the input device has a touch screen display. The inputdevice can include a cursor control, such as a mouse, a trackball, orcursor direction keys, for communicating direction information andcommand selections to the processor and for controlling cursor movementon the display.

According to various implementations, the processes described herein canbe implemented by the computing system in response to the processorexecuting an arrangement of instructions contained in main memory. Suchinstructions can be read into main memory from another computer-readablemedium, such as the storage device. Execution of the arrangement ofinstructions contained in main memory causes the computing system toperform the illustrative processes described herein. One or moreprocessors in a multi-processing arrangement may also be employed toexecute the instructions contained in main memory. In alternativeimplementations, hard-wired circuitry may be used in place of or incombination with software instructions to effect illustrativeimplementations. Thus, implementations are not limited to any specificcombination of hardware circuitry and software.

According to various embodiments, tracking eye movement may be performedusing any suitable device such as, for example, an Eyelink® 1000binocular eye tracker (500 Hz sampling, SR Research). The suitabledevice, i.e. the eye tracker, may be stationary or portable. The eyetracking movement samples may be obtained at any suitable frequency,such as for instance, 10 Hz to 10,000 Hz or more. The subject may bepositioned an appropriate distance from the device, such as, forexample, 10, 20, 30, 40, 50, 55, 60, 70, 80, 90 cm or more, or even ameter or more from the device screen. In some instances, the subject'shead may be stabilized, such as, for instance by using a chinrest orsimilar stabilizing mechanism. The subject may be seated or reclining.Preferably, the presentation monitor of the device is adjusted so as tosubstantially match the subject's gaze direction. The tracking eyemovement may be performed for a total of, for example, 30, 60, 90, 120,150, 180, 200, 220, 240, 270, 300, 330, 360, 400, 450, 500 seconds ormore, or for 5, 10, 15, 20, 25, 30, 45, 60, or 90 minutes or more. Assuch, according to the methods provided, 1,000, 5,000, 10,000, 20,000,25,000, 50,000, 75,000, 100,000, 150,000, 200,000, 250,000, 300,000 ormore samples of eye position may be obtained. Similarly, the trackingeye movement may be performed using a video oculography device, such as,for instance, goggles, or using a web-cam based tracking system.

According to various embodiments, analyzing eye movement may beperformed by any suitable means. In some instances, a stimulus and ananalysis stream are provided that allows interpreting raw eye positiondata. In some instances, an algorithm may be provided for looking atpupil position directly thereby yielding information about ocularmotility. Preferably, a device is adapted into a novel mobile systemthat may analyze eye movement close in time or substantially concurrentto the eye movement itself.

According to various embodiments, eye movement may be tracked inresponse to a visual stimulus. In some instances, the visual stimulusmay be, for instance, a video such as a music video that may move, forinstance clockwise, along the outer edge, of a computer monitor. In someinstances, such a video may be provided starting at the upper or lower,left or right hand corners, of a screen. The visual stimulus such as avideo, e.g. a music video, may be provided in a substantially squareaperture with an area of approximately 10, 12, 14, 16, 18, 20, 25, ordegrees, for example, approximately 1/10, ⅛, ⅙, ⅕, ¼, ⅓, ½ of the sizeof the screen or so. The visual stimulus, such as, for example a musicvideo, may play substantially continuously during the eye movementtracking, and it may in some instances move across the screen at arelatively or substantially constant speed. For instance, such a visualstimulus, for instance, a music video may cover each edge of a monitorin about 2, 5, 10, 15, 20, 30, 45 or 60 seconds or so. Therefore, insome instances, a full cycle may take, for instance, 10, 20, 30, 40, 50,60, 75, 100, 120, 150, 180 seconds or so. Multiple cycles of such avisual stimulus, for instance a music video may be played, for instance,one, two, three, four, five, six, seven, eight, nine, ten, twelve,fifteen, twenty or more full cycles. As such, the visual stimulus may beprovided, the eye movement may be tracked, in effect, in some instancesthe video may be played for a total of, for example, 30, 60, 90, 120,150, 180, 200, 220, 240, 270, 300, 330, 360, 400, 450, 500 seconds ormore. In instances where the visual stimulus is in the form of a video,a countdown video may be played in the starting position for, forinstance, 5, 10, 15, 20, 25, or 30 seconds or more before beginning thevisual stimulus, e.g. video, to provide subjects sufficient time toorient to the visual stimulus. Likewise, the visual stimulus, forinstance a video, may be continued for an addition 2, 5, 10, 15, 20, 30,45 or 60 seconds or so after the eye movement tracking is performed toreduce or substantially avoid boundary effects. The same result could beobtained by having the visual stimulus moving over any distance xrelative to any amount of time t. The ideal stimulus would move howeverin the both the x and y Cartesian planes to optimize the assessmentcapability of the method.

Tracking eye movement may feature generating figures substantiallyresembling boxes that reflect the trajectory traveled by the visualstimulation, such as when it moves across a screen. In healthy controls,these figures substantially resembling boxes may look like, forinstance, substantially equilateral rectangles or squares, reflectingthe trajectory traveled by the visual stimulus across a screen. Ininstances of structural and non-structural traumatic brain injury,neurological damage or increased intracranial pressure, such figures maynot substantially resemble a box, a rectangle or a square. In fact, insome instances, the cranial nerve having reduced or impaired function orconduction may be identified. In some instances, the figures generatedthat reflect the trajectory traveled by the visual stimulation maydemonstrate abnormal distribution of or absence of normal plotting pairsin particular areas. Increased variability along the y-axis may forexample reflect cranial nerve II dysfunction. Decreased variabilityalong the y-axis, or decreased height to width ratio may reflect CN IIIdysfunction. Increased height to width ratio may reflect CN IV or VIdysfunction. The height of the box may be mathematically determined byassessing the position of the pupil as the video traverses the top andbottom of the presented visual stimulus. This “actual” height may bedifferent from the perceived height mathematically, since the perceivedheight can represent aberrant pupillary motion due to the patient'socular motility dysfunction. The integrity of the box walls may also beindicative of other types of dysfunction. Both cranial nerve palsies andmass effect may cause defects in box trajectory. CN III defects mayimpact the top and/or bottom of the box. CN VI palsies may impact thesides of the box.

Eye movement may also be tracked without using a moving stimulus. It ispossible to assess eye movement without having the stimulus move at all,but by assessing the x, y coordinates over times during naturalisticviewing. For example, eye movement may be tracked during televisionwatching or live viewing of an environment without a specific viewingapparatus such as a monitor or screen.

DETAILED DESCRIPTION

Before the present methods are described, it is to be understood thatthis invention is not limited to particular methods and experimentalconditions described, as such methods and conditions may vary. It isalso to be understood that the terminology used herein is for purposesof describing particular embodiments only, and is not intended to belimiting, since the scope of the present invention will be limited onlyby the appended claims. As used in this specification and the appendedclaims, the singular forms “a”, “an”, and “the” include pluralreferences unless the context clearly dictates otherwise. Thus, forexample, references to “the method” includes one or more methods, and/orsteps of the type described herein and/or which will become apparent tothose persons skilled in the art upon reading this disclosure and soforth in their entirety.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Although any methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of the invention, the preferred methods andmaterials are now described. All publications mentioned herein areincorporated herein by reference I their entireties.

Definitions

The terms used herein have the meanings recognized and known to those ofskill in the art, however, for convenience and completeness, particularterms and their meanings are set forth below.

“Subject” or “patient” refers to a mammal, preferably a human,undergoing treatment or screening for a condition, disorder or diseasesuch as, for instance, any condition characterized by or featuringdisconjugate gaze or strabismus.

By “assessing or quantitating structural and non-structural traumaticbrain injury” is meant identifying, diagnosing, or determining theseverity a traumatic brain injury such as, for instance, concussion,subconcussion or blast injury.

By “localizing a central nervous system lesion” is meant in someinstances determining information that may predict a likely position ofa lesion, for instance, determining the side of the body, for instance,left or right, where a lesion may likely be located within the centralnervous system. In other instances, “localizing a central nervous systemlesion” may mean determining a particular fossa or compartment, such as,for instance, a fascia compartment or brain ventricle in which a lesionis likely located within the central nervous system.

By “having eye movement of a first eye that is significantly differentfrom eye movement of a second eye” is meant displaying eye movement in afirst eye over 5, 10, 25, 50, 100, 1,000, 5,000, 10,000 or moreobservations, tracked with at least x, y coordinate positions, that isat least 5%, 10%, 15%, 20%, 25%, 30%, 40%, 50%, 75%, or 100% or morevariant compared to the corresponding eye movement observations trackedfrom the second eye. The 5%, 10%, 15%, 20%, 25%, 30%, 40%, 50%, 75%, or100% or more variant may be calculated or observed either numerically orgraphically. Alternatively, “having eye movement of a first eye that issignificantly different from eye movement of a second eye” is meantdisplaying eye movement in a first eye over 5, 10, 25, 50, 100, 1,000,5,000, 10,000 or more observations, tracked with at least x, ycoordinate positions, that, when graphically displayed in a scatterplotas described herein, is at least 5°, 10°, 15°, 20°, 25°, 30°, 40°, 50°,60°, 75° or 90° or more variant compared to the corresponding eyemovement observations tracked and graphically displayed on a scatterplotas described herein from the second eye.

Eye movement tracking for neuropsychiatric and brain injury research(Heitger, et al., Brain, 2009; 132: 2850-2870; Maruta, et al., J HeadTrauma Rehabil., 2010; 25: 293-305) has been performed for nearly 30years and can evaluate smooth pursuit, saccades, fixation, pupil sizeand other aspects of gaze. Spatial calibration of the eye tracker isgenerally performed for each individual being tracked. With calibration,the eye-tracker measures the relative position of pupil and cornealreflection for a period of about 400-800 ms while the subject looks at atarget or targets of known position to generate meaningful spatialcoordinates during subsequent pupil movement. The process of spatialcalibration implies relatively preserved neurologic function because itrequires that the subject is able to follow commands and look atspecific points.

The process of spatial calibration may mask deficits in ocular motility.If there is a persistent and replicable weakness in movement of an eye,the camera may interpret the eye's ability to move in the direction ofthat weakness as the full potential range of motion in that directiondue to the calibration process. In other words if the subject isdirected to look at a position but consistently only moves halfwaythere, the calibration process may account for that when trackingsubsequent eye movements and interpret movements to the halfway point asoccurring at the full range of normal motion. If during calibration oneeye only makes it half-way to the target, but the other eye is fullythere, the camera may interpret both eyes as being together when oneperforms half the eye movement as the other. Thus binocular spatialcalibration may preclude detection of disconjugate gaze unless each eyeis calibrated separately using a dichoptic apparatus (Schotter, et al.,PLoS One, 2012; 7: e35608).

The present invention may use a novel technique for non-spatiallycalibrated tracking performed while subjects watch a music video movinginside an aperture on a computer monitor. The aperture moves around themonitor periphery at a known rate so that the position of the pupil canbe predicted at any given time based on the time elapsed since the startof the video. By using elapsed time, rather than spatial calibration,the method detects impaired ability to move one pupil relative to theother. Uncalibrated tracking not only does not compensate for impairedmotility, but also can be used in patients who do not follow commandssuch as aphasics, foreign-language speakers, persistently vegetativeindividuals and small children. It can also be used on animals.

If the subject's eyes are positioned about 55 cm from the center of the30×35 cm viewing monitor, the method and associated algorithm elicitspupil movement in a maximum range of about 15° in any direction frommidposition, or approximately 30° total from top to bottom or side toside. Thus, in some instances, the method and associated algorithm maynot require or assess the full range of ocular motility, nor the entirevisual field. Use of a larger monitor, or one positioned closer to thesubject would enable assessment of these.

The technique described herein differs from uncalibrated tracking usingstatic stimuli for on-target and off-target fixations in a population ofminimally conscious and persistently vegetative patients that have openeyes (Trojano, et al., J Neurol., 2012 (published online; ahead ofprint)). The moving images shown within an aperture that movesperiodically allow assessing both coarse and fine eye movementcharacteristics in both controls and neurologically impaired subjects.Unlike other studies (Contreras, et al., Brain Res., 2011; 1398: 55-63;Contreras, et al., J Biol Phys., 2008; 34: 381-392; Maruta, et al., JHead Trauma Rehabil., 2010; 25: 293-305; Trojano, et al., J Neurol.,2012 (published online; ahead of print)) the present methods do not usesaccade count or spatial accuracy which requires transformation of rawdata by a series of scaling and rotating processes whose effectivenessdepends on the ability of their subjects to follow precise commandsreliably. The present methods also differ from gaze estimation, whichrequires either a fixed head position or multiple light sources andcameras to localize the pupil (Guestrin, et al., IEEE Trans Biomed Eng.,2006; 53: 1124-1133).

Video oculography is a relatively newer technique that uses infraredcameras mounted in goggles to track the center of the pupil's positionas the eye moves. It has been demonstrated to be useful in screening forneurovestibular and labyrinthine dysfunction and most recently indistinguishing these from vertebrobasilar stroke (Newman-Toker, et al.,Stroke, 2013; 44: 1158-1161). Video oculography generally relies onspatial calibration (Hong, et al., Behav Res Methods, 2005; 37: 133-138;Schreiber, et al., IEEE Trans Biomed Eng., 2004; 51: 676-679). The useof our non-calibrated stimulus algorithm with video oculography ratherthan a sole eye tracking camera might be an interesting subject forfuture study.

The methods described herein provide both sensitivity and specificity.Because so many different cortical functions are required for watching avideo, any process impeding global cranial function or specific cranialnerve function will likely be revealed by the present methods. Trackingmay be confounded in patients with a history of prior brain insult, whoare intoxicated, or are under the influence of pharmacologic agents.Patients' cognitive abilities, attention span and distractibility willimpact the quality of ocular motility data.

Binocular Eye Movement Monitoring

When the human brain is physiologically intact, the eyes move togetherwith a conjugate gaze. Only by deliberate conscious effort can anindividual overcome this mechanism (eg when they deliberately “cross”the eyes.) A failure of the eyes to move in complete synchrony is calleddisconjugate gaze.

Binocular tracking may be used to compare the non-spatially calibratedtrajectory of one eye to the other. Subtle differences between thetrajectories of the two eyes may be detected. These differences providevaluable information regarding the physiologic function or dysfunctionof the movement of one eye relative to the other. In the absence ofknown structural ocular injury, such differences reflect physiologicdifferences in the function of the two sides of the brain. Since brainlesions due to stroke, trauma or concussion, tumors, demyelinatingdisease, hydrocephalus, degenerative disease, etc. are rarely completelysymmetric, comparing the eye movement of one eye to the eye movement ofthe other eye may be used to either confirm the presence of a lesion, todifferentiate the existence of a lesion from other more global factorsthat may affect a person's ability to participate in an eye trackingtask, such as fatigue, intoxication, medications, drug abuse,malingering, or lack of willingness to participate in an eye trackingtask.

Thus binocular tracking and directly comparing the trajectories obtainedover time, rather than with spatial calibration, may be used to diagnosepathology and to distinguish between these diagnoses and global factorsthat may impact eye tracking. In addition to or instead of an eyetracking camera, a video oculography device such as goggles may be usedto evaluate eye movements over time rather than with spatialcalibration. The eye tracking device may also be located remotely andfunction via the internet or other visualization mechanism.

Computing System

A computing system according to the invention is described herein.Implementations of the observer matter and the functional operationsdescribed herein can be implemented in other types of digital electroniccircuitry, or in computer software, firmware, or hardware, including thestructures disclosed in this specification and their structuralequivalents, or in combinations of one or more of them. The computersystem or computing device 1000 can be used to implement a device thatincludes the processor 106 and the display 108, the eye movement/gazetracker component 104, etc. The computing system 1000 includes a bus1005 or other communication component for communicating information anda processor 1010 or processing circuit coupled to the bus 1005 forprocessing information. The computing system 1000 can also include oneor more processors 1010 or processing circuits coupled to the bus forprocessing information. The computing system 1000 also includes mainmemory 1015, such as a random access memory (RAM) or other dynamicstorage device, coupled to the bus 1005 for storing information, andinstructions to be executed by the processor 1010. Main memory 1015 canalso be used for storing position information, temporary variables, orother intermediate information during execution of instructions by theprocessor 1010. The computing system 1000 may further include a readonly memory (ROM) 1010 or other static storage device coupled to the bus1005 for storing static information and instructions for the processor1010. A storage device 1025, such as a solid state device, magnetic diskor optical disk, is coupled to the bus 1005 for persistently storinginformation and instructions.

The computing system 1000 may be coupled via the bus 1005 to a display1035, such as a liquid crystal display, or active matrix display, fordisplaying information to a user. An input device 1030, such as akeyboard including alphanumeric and other keys, may be coupled to thebus 1005 for communicating information and command selections to theprocessor 1010. In another implementation, the input device 1030 has atouch screen display 1035. The input device 1030 can include a cursorcontrol, such as a mouse, a trackball, or cursor direction keys, forcommunicating direction information and command selections to theprocessor 1010 and for controlling cursor movement on the display 1035.

According to various implementations, the processes described herein canbe implemented by the computing system 1000 in response to the processor1010 executing an arrangement of instructions contained in main memory1015. Such instructions can be read into main memory 1015 from anothercomputer-readable medium, such as the storage device 1025. Execution ofthe arrangement of instructions contained in main memory 1015 causes thecomputing system 1000 to perform the illustrative processes describedherein. One or more processors in a multi-processing arrangement mayalso be employed to execute the instructions contained in main memory1015. In alternative implementations, hard-wired circuitry may be usedin place of or in combination with software instructions to effectillustrative implementations. Thus, implementations are not limited toany specific combination of hardware circuitry and software.

Implementations of the observer matter and the operations describedherein can be implemented in digital electronic circuitry, or incomputer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them. The observer matter describedherein can be implemented as one or more computer programs, i.e., one ormore modules of computer program instructions, encoded on one or morecomputer storage media for execution by, or to control the operation of,data processing apparatus. Alternatively or in addition, the programinstructions can be encoded on an artificially-generated propagatedsignal, e.g., a machine-generated electrical, optical, orelectromagnetic signal that is generated to encode information fortransmission to suitable receiver apparatus for execution by a dataprocessing apparatus. A computer storage medium can be, or be includedin, a computer-readable storage device, a computer-readable storagesubstrate, a random or serial access memory array or device, or acombination of one or more of them. Moreover, while a computer storagemedium is not a propagated signal, a computer storage medium can be asource or destination of computer program instructions encoded in anartificially-generated propagated signal. The computer storage mediumcan also be, or be included in, one or more separate components or media(e.g., multiple CDs, disks, or other storage devices). Accordingly, thecomputer storage medium is both tangible and non-transitory.

The operations described herein can be performed by a data processingapparatus on data stored on one or more computer-readable storagedevices or received from other sources.

The term “data processing apparatus” or “computing device” encompassesall kinds of apparatus, devices, and machines for processing data,including by way of example a programmable processor, a computer, asystem on a chip, or multiple ones, or combinations of the foregoing.The apparatus can include special purpose logic circuitry, e.g., an FPGA(field programmable gate array) or an ASIC (application-specificintegrated circuit). The apparatus can also include, in addition tohardware, code that creates an execution environment for the computerprogram in question, e.g., code that constitutes processor firmware, aprotocol stack, a database management system, an operating system, across-platform runtime environment, a virtual machine, or a combinationof one or more of them. The apparatus and execution environment canrealize various different computing model infrastructures, such as webservices, distributed computing and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor data (e.g., one or more scripts stored in a markup languagedocument), in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more modules,sub-programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers that are locatedat one site or distributed across multiple sites and interconnected by acommunication network.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of a computer area processor for performing actions in accordance with instructions andone or more memory devices for storing instructions and data. Generally,a computer will also include, or be operatively coupled to receive datafrom or transfer data to, or both, one or more mass storage devices forstoring data, e.g., magnetic, magneto-optical disks, or optical disks.However, a computer need not have such devices. Moreover, a computer canbe embedded in another device, e.g., a mobile telephone, a personaldigital assistant (PDA), a mobile audio or video player, a game console,a Global Positioning System (GPS) receiver, or a portable storage device(e.g., a universal serial bus (USB) flash drive), to name just a few.Devices suitable for storing computer program instructions and datainclude all forms of non-volatile memory, media and memory devices,including by way of example semiconductor memory devices, e.g., EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, implementations of the observermatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input.

Described herein are many specific implementation details, these shouldnot be construed as limitations on the scope of any inventions or ofwhat may be claimed, but rather as descriptions of features specific toparticular implementations of particular inventions. Certain featuresdescribed herein in the context of separate implementations can also beimplemented in combination in a single implementation. Conversely,various features described in the context of a single implementation canalso be implemented in multiple implementations separately or in anysuitable subcombination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the implementations described above should not beunderstood as requiring such separation in all implementations, and itshould be understood that the described program components and systemscan generally be integrated in a single software product or packagedinto multiple software products.

The Relationship of Aspect Ratio and Variance as Measures of the Signal.

When the (x, y) pairs are plotted to show the ‘box plots,’ they havebeen preprocessed because the absolute values of the raw data are oflimited use since changes in the signal over time are most important.There are many ways to normalize data, including dividing by the mean,by the standard deviation, or by the variance. Furthermore, the standarddeviation or variance can be computed for all the data at once or x canbe normalized using the variance of x and y can be normalized using thevariance of y. Any normalization procedure for periodic data likelyincludes subtracting the mean, so the signal can be plotted as signalchange alternating around zero. All of these transformations areconventional and widely used in data analysis by those of ordinary skillin the art. The details depend on the question being asked and the typeof modeling or statistical testing being used.

In creating the box plots described herein, the raw data is preprocessedas follows: for the x (horizontal) and y (vertical) vectorsindependently, the mean is subtracted and divided by the standarddeviation (which is the square root of the variance). This puts all thedata in the same relative frame (zero-mean, max and min about 1 and −1).This is the reason the boxes look square (even if the stimuluspresentation monitor is not square).

This means that ‘long’ and ‘short’ sides are reflecting relativevariability. If the variability is high, the denominator is high and themeasure value low. So, for example, if the variability of the horizontal(x) data is high relative to the variability of the vertical (y) data,the horizontal aspect of the box will be relatively smaller, and theresult will be a tall skinny box (higher aspect ratio). Conversely, ifthe variability of the vertical (y) data is high relative to thevariability of the horizontal (x) data, the vertical range will bereduced and the result will be a short fat box (lower aspect ratio)(FIG. 4).

Thus, particular implementations of the observer matter have beendescribed. Other implementations are within the scope of the followingclaims. In some cases, the actions recited in the claims can beperformed in a different order and still achieve desirable results. Inaddition, the processes depicted in the accompanying figures do notnecessarily require the particular order shown, or sequential order, toachieve desirable results. In certain implementations, multitasking andparallel processing may be advantageous.

Conjugacy of Eye Movement

The methods described herein may identify strabismus. In a population of14,006 consecutive patients examined at a pediatric eye clinic in Rome,2.72% demonstrated either A or V-pattern strabismus (Dickmann, et al.,Ophthalmic Epidemiol., 2012; 19: 302-305). A-pattern was associated witha greater prevalence of neurological impairment, hydrocephalus andmeningomyelocele, while those with V-pattern exhibited a greaterprevalence of craniosynostosis and malformative syndromes (Dickmann, etal., Ophthalmic Epidemiol., 2012; 19: 302-305). Delays in treatment ofstrabismus onset following binocular vision maturation may be associatedwith permanent disruption of stereopsis and sensory fusion (Fawcett,Curr Opin Ophthalmol., 2005; 16: 298-302).

Given the relatively low prevalence of strabismus, the methods describedherein are useful for the rapid automated assessment of acquireddisconjugacy. Such disconjugacy may be due to neurologic causesincluding trauma, hydrocephalus, demyelination, inflammation, infection,degenerative disease, neoplasm/paraneoplastic syndrome, metabolicdisease including diabetes, or vascular disruption such as stroke,hemorrhage or aneurysm formation. Disconjugacy may also be due toophthalmologic causes such as conjunctivitis, ophthalmoplegia, ocularinjury or other diseases. The methods described herein may featureassessing conjugacy or disconjugacy of eye movement in correlation withstructural and non-structural traumatic brain injury, includingconcussion or blast injury.

Structurally and Non-Structurally Brain Injured Subjects

A purpose of the prospective observational study described herein was toquantitate differences in eye tracking of structurally andnon-structurally brain injured subjects relative to non-brain but bodilyinjured and healthy non-injured controls to identify the eye trackingparameters associated with structural and non-structural injury. Anotherpurpose was to identify a correlation between impaired eye tracking andclinical neurologic functioning. Eye tracking and clinical concussionassessments were performed on 44 injured subjects, and eye tracking wasperformed only on 31 healthy normal controls. 51 eye tracking parameterswere assessed in each patient. 10 parameters showed statisticallysignificant differences between negative controls (healthy normal peopleand corporally injured trauma patients) and both positive controls(patients with structural brain injury) and patients with non-structuralbrain injury. 8 additional parameters showed statistically significantdifferences between negative controls (healthy normal people andcorporally injured trauma patients) and patients with either structuralor non-structural brain injury. 10 of the eye tracking measures showedstatistically significant correlation between SCAT or SAC scores,demonstrating that these eye tracking parameters correlated with avalidated clinical outcome measure.

In order to assess ocular motility including the function of cranialnerves III, IV, and VI and associated nuclei, a novel technique forautomated eye movement tracking was developed using temporal rather thanspatial calibration. The position of the pupil is predicted based ontime elapsed since the start of the video rather than spatialcalibration, enabling detection of impaired ability to move the pupilrelative to normal controls or the opposite eye. Temporal calibrationoffers the additional advantage of utility to populations that may notbe willing or able to cooperate with calibration instructions such asyoung children, foreign-language speakers, minimally conscious persons,or aphasics.

The data presented herein quantitates differences in eye tracking ofstructurally and non-structurally brain injured subjects relative tonon-brain but bodily injured and healthy non-injured controls toidentify the parameters associated with structural and non-structuralinjury. The data presented herein further establish a correlationbetween impaired eye tracking and clinical neurologic functioning.

General Definitions

Raw x and y cartesian coordinates of pupil position are collected andstored in a one-dimensional vector:x _(i) ₁   (1)y _(i) ₁   (2)

This data is normalized according to the following form:

$\begin{matrix}{{{\overset{\_}{x}}_{i} = \frac{x_{i} - {{Mean}\;(x)}}{\sigma_{x}}},} & (3) \\{{{\overset{\_}{y}}_{i} = \frac{y_{i} - {{Mean}\;(y)}}{\sigma_{y}}},} & (4)\end{matrix}$

Index i corresponds to an individual data point. The size of i dependson the eye tracking hardware capture frequency and the time of tracking.The data is then sorted by eye (j=1:2, left, right), cycle (currentstimulus method features an aperture that moves around the computerscreen for five cycles) (k=1:5, first, second, third, fourth, fifth) andbox segment (l=1:4, top, right, bottom, left). Implicit, is that each j,k, l has its own data points, n, whose size is also governed by thehardware tracking frequency and time length.x _(i) →x _(j,k,l),  (5)y _(i) →y _(j,k,l),  (6)Individual MetricsSegment Meanx _(j,k,l),  (7)y _(j,k,l),  (8)

Corresponds to the arithmetic average of all data points on each segmentl for all j, k. The result is one number representing each segment l.

Median

Corresponds to the statistical median of all data points on each segmentl for all j, k. The result is one number representing each segment l.{tilde over (x)} _(j,k,l),  (9){tilde over (y)} _(j,k,l),  (10)Segment VarianceVar( x _(j,k,l)),  (11)Var( y _(j,k,l)),  (12)

Corresponds to the statistical variance of all data points on eachsegment l for all j, k. The result is one number representing eachsegment l.

Specific MetricsL. varYtop=Var( y _(1,average k=1:5,1))  (13)R. varYtop=Var( y _(2,average k=1:5,1))  (14)L. varXrit=Var( x _(1,average k=1:5,2))  (15)R. varXrit=Var( x _(2,average k=1:5,2))  (16)L. varYbot=Var( y _(1,average k=1:5,3))  (17)R. varYbot=Var( y _(2,average k=1:5,3))  (18)L. varXlef=Var( x _(1,average k=1:5,4))  (19)L. varXlef=Var( y _(2,average k=1:5,4))  (20)L. varTotal=Average(Var( x _(1,average k=1:5))+Var( y_(1,average k=1:5)))  (21)R. varTotal=Average σ _(x) _(j,k,l′)   (23)σ _(x) _(j,k,l′)   (24)

Corresponds to the statistical standard deviation of all data points oneach segment l for all j, k. The result is one number representing eachsegment l.

Segment SkewSkew( x _(j,k,l))= {tilde over (x)} _(j,k,l) −{tilde over (x)}_(j,k,l),  (25)Skew( y _(j,k,l))= {tilde over (y)} _(j,k,l) −{tilde over (y)}_(j,k,l),  (26)

Corresponds to the statistical skew (how far the mean is from themedian) of all data points on each segment l for all j, k. The result isone number representing each segment l.

Specific MetricsL. SkewTop=Skew( y _(1,average k−1:5,1))  (27)R. SkewTop=Skew( y _(2,average k−1:5,1))  (28)L. SkewRit=Skew( x _(1,average k−1:5,2))  (29)R. SkewRit=Skew( x _(2,average k−1:5,2))  (30)L. SkewBot=Skew( y _(1,average k−1:5,3))  (31)R. SkewBot=Skew( y _(2,average k−1:5,3))  (32)L. SkewLef=Skew( x _(1,average k−1:5,4))  (33)R. SkewLef=Skew( x _(2,average k−1:5,4))  (34)Segment Normalized Skew

$\begin{matrix}{{{{SkewNorm}\left( {\overset{\_}{x}}_{j,k,l} \right)} = \frac{{Skew}\mspace{11mu}\left( {\overset{\_}{x}}_{j,k,l} \right)}{\sigma_{{\overset{\_}{x}}_{j,k,l}}}},} & (35) \\{{{{SkewNorm}\left( {\overset{\_}{y}}_{j,k,l} \right)} = \frac{{Skew}\mspace{11mu}\left( {\overset{\_}{y}}_{j,k,l} \right)}{\sigma_{{\overset{\_}{y}}_{j,k,l}}}},} & (36)\end{matrix}$Specific MetricsL. SkewTopNorm=SkewNorm( y ₁,average k=1:5,1)  (37)R. SkewTopNorm=SkewNorm( y ₂,average k=1:5,1)  (38)L. SkewRitNorm=SkewNorm( x ₁,average k=1:5,2)  (39)R. SkewRitNorm=SkewNorm( x ₂,average k=1:5,2)  (40)L. SkewBotNorm=SkewNorm(y ₁,average k=1:5,3)  (41)R. SkewBotNorm=SkewNorm(y ₂,average k=1:5,3)  (42)L. SkewLefNorm=SkewNorm( x ₁,average k=1:5,4)  (43)R. SkewLefNorm=SkewNorm( x ₂,average k=1:5,4)  (44)Box HeightBoxHeight_(j,k) =y _(j,k,l) −y _(j,k,3)  (45)Box WidthBoxHeight_(j,k) =x _(j,k,2) −x _(j,k,4)  (46)Box Aspect Ratio

$\begin{matrix}{{AspectRatio}_{j,k} = \frac{{BoxHeight}_{j,k}}{{BoxWidth}_{j\; \cdot \; k}}} & (47)\end{matrix}$Box AreaBoxArea_(j,k)=BoxHeight_(j,k)xBoxWidth_(j,k)  (48)Conjugacy

The five cycles are averaged together to give one averaged cycle,rendering:x _(j,l),  (49)y _(j,l),  (50)

Then the data from the right eye is subtracted from the left eye toobtain a delta value:{circumflex over (x)} _(l) =x _(1,l) −x _(2,l)  (51)ŷ _(l) =y _(1,l) −y _(2,l)  (52)

Here x represents the left normalized raw x pupil position minus theright normalized raw x pupil position. l corresponds to the top, right,bottom and left segments of the box.

Variance (Conjugacy)

The variance here does not follow the traditional form of statisticalvariance. In the traditional form, the average of the data points issubtracted from the sum of individual data points. In this case, theaverage is forced to zero, thus inferring that the hypothetical controlpatient has perfect conjugacy (left and right eye move preciselytogether).

$\begin{matrix}{{{{Conj}\mspace{11mu}{var}\; X} = {{{Var}\;\left( \hat{x} \right)} = \frac{{\sum\limits_{l = 1}^{4}\left( {\hat{x}}_{1} \right)^{2}} - 0}{\sum\limits_{l = 1}^{4}{\hat{x}}_{1}}}},} & (53) \\{{{{Conj}\mspace{11mu}{var}\; Y} = {{{Var}\;\left( \hat{y} \right)} = \frac{{\sum\limits_{l = 1}^{4}\left( {\hat{y}}_{1} \right)^{2}} - 0}{\sum\limits_{l = 1}^{4}{\hat{y}}_{1}}}},} & (54) \\{{TotalVariance} = {{{Conj}\mspace{11mu}{{tot}{Var}}} = {{{Var}\mspace{11mu}\left( \hat{x} \right)} = {{Var}\mspace{11mu}\left( \hat{y} \right)}}}} & (55) \\{{{CoVariance} - {{Conj}\mspace{11mu}{Corr}\;{XY}}} = \frac{\sum\limits_{l = 1}^{4}{{\hat{x}}_{l}{\hat{y}}_{l}}}{{\sum\limits_{l = 1}^{4}{\hat{x}}_{l}} - 1}} & (56)\end{matrix}$Specific Metrics

$\begin{matrix}{{{{Conj}\mspace{14mu}{var}\; X\;{top}} = \frac{{\sum\left( {\hat{x}}_{1} \right)^{2}} - 0}{\sum{\hat{x}}_{1}}},} & (57) \\{{{{Conj}\mspace{14mu}{var}\; X\;{rit}} = \frac{{\sum\left( {\hat{x}}_{2} \right)^{2}} - 0}{\sum{\hat{x}}_{2}}},} & (58) \\{{{{Conj}\mspace{14mu}{var}\; X\;{bot}} = \frac{{\sum\left( {\hat{x}}_{3} \right)^{2}} - 0}{\sum{\hat{x}}_{3}}},} & (59) \\{{{{Conj}\mspace{14mu}{var}\; X\;{lef}} = \frac{{\sum\left( {\hat{x}}_{4} \right)^{2}} - 0}{\sum{\hat{x}}_{4}}},} & (60) \\{{{{Conj}\mspace{14mu}{var}\; Y\;{top}} = \frac{{\sum\left( {\hat{y}}_{1} \right)^{2}} - 0}{\sum{\hat{y}}_{1}}},} & (61) \\{{{{Conj}\mspace{14mu}{var}\; Y\;{rit}} = \frac{{\sum\left( {\hat{y}}_{2} \right)^{2}} - 0}{\sum{\hat{y}}_{2}}},} & (62) \\{{{{Conj}\mspace{14mu}{var}\; Y\;{bot}} = \frac{{\sum\left( {\hat{y}}_{3} \right)^{2}} - 0}{\sum{\hat{y}}_{3}}},} & (63) \\{{{{Conj}\mspace{14mu}{var}\; Y\;{rit}} = \frac{{\sum\left( {\hat{y}}_{4} \right)^{2}} - 0}{\sum{\hat{y}}_{4}}},} & (64) \\{{{{Conj}\mspace{14mu}{Corr}\;{XY}\;{top}} = \frac{\sum{{\hat{x}}_{1}{\hat{y}}_{1}}}{{\sum{\hat{x}}_{1}} - 1}},} & (65) \\{{{{Conj}\mspace{14mu}{Corr}\;{XY}\;{rit}} = \frac{\sum{{\hat{x}}_{2}{\hat{y}}_{2}}}{{\sum{\hat{x}}_{2}} - 1}},} & (66) \\{{{{Conj}\mspace{14mu}{Corr}\;{XY}\;{bot}} = \frac{\sum{{\hat{x}}_{3}{\hat{y}}_{3}}}{{\sum{\hat{x}}_{3}} - 1}},} & (67) \\{{{{Conj}\mspace{14mu}{Corr}\;{XY}\;{lef}} = \frac{\sum{{\hat{x}}_{4}{\hat{y}}_{4}}}{{\sum{\hat{x}}_{4}} - 1}},} & (68)\end{matrix}$Variance x Ratio Top/Bottom (Conjugacy)

$\begin{matrix}{{{{Conj}\mspace{14mu}{var}\; X\;{topbotRatio}} = \frac{{Var}\left( {\hat{x}}_{1} \right)}{{Var}\left( {\hat{x}}_{3} \right)}}\;} & (69)\end{matrix}$Variance y Ratio Top/Bottom (Conjugacy)

$\begin{matrix}{{{{Conj}\mspace{14mu}{var}\; Y\;{topbotRatio}} = \frac{{Var}\left( {\hat{y}}_{1} \right)}{{Var}\left( {\hat{y}}_{3} \right)}}\;} & (70)\end{matrix}$Variance x Ratio Left/Right (Conjugacy)

$\begin{matrix}{{{{Conj}\mspace{14mu}{var}\;{XlefritRatio}} = \frac{{Var}\left( {\hat{x}}_{4} \right)}{{Var}\left( {\hat{x}}_{2} \right)}}\;} & (71)\end{matrix}$Variance y Ratio Left/Right (Conjugacy)

$\begin{matrix}{{{{Conj}\mspace{14mu}{var}\;{YlefritRatio}} = \frac{{Var}\left( {\hat{y}}_{4} \right)}{{Var}\left( {\hat{y}}_{2} \right)}}\;} & (72)\end{matrix}$

The following example is set forth to provide those of ordinary skill inthe art with a description of how to make and use the methods, kits andcompositions of the invention, and are not intended to limit the scopethereof. Efforts have been made to insure accuracy of numbers used(e.g., amounts, temperature, etc.), but some experimental errors anddeviations should be accounted for. Unless indicated otherwise, partsare parts by weight, molecular weight is average molecular weight,temperature is in degrees Centigrade, and pressure is at or nearatmospheric.

EXAMPLE

Abstract

INTRODUCTION: Objective diagnosis of concussion remains a challenge. Eyemovements tracked at a very high frequency (˜500 Hz) can detectabnormalities that last only a fraction of second. We mathematicallyconverted abnormalities in eye movements related to concussion into amodel that predicts the probability of being concussed in a pediatricpatient population.

METHODS: This prospective case-control study recruited concussed andhealthy control children from a concussion referral center. Eyemovements were recorded while children watched a 220 second video clipas it rotated clockwise around the periphery of a 17″-viewing monitor.The pupils' raw coordinates were processed to obtain metrics thatincluded measures from each eye separately and from both eyes together.Concussed patients were also evaluated clinically by performingconvergence tests.

RESULTS: There were 32 age and gender matched subjects in each group(ages 4-21; mean 13; p-value for age-matching=0.979). Eye tracking datawas used to build an optimal model that predicts the probability ofconcussion as defined by the CDC. Accurate detection as demonstrated byan area under the curve of 0.85 (sensitivity of 72% and specificity of84%) was achieved. Clinical identification of abnormal near point ofconvergence also correlated with eye tracking (AUC=0.81)

CONCLUSION: Eye tracking correlates with clinical evaluations ofconvergence and can be used as a diagnostic tool for concussion in apediatric concussion referral center population.

Subject selection: Controls were siblings of children visiting theneurosurgery clinic. Inclusion criteria for cases were less than 22years of age, intact ocular motility, vision correctable to within20/500 bilaterally, ability to provide a complete ophthalmologic,medical and neurologic history, medications consumed within past 24 hrs,and concussion within past 3 years. Parents were asked to corroboratedetails of the above for children aged 4-17 years of age. Controls wereselected for the study if they were less than 22 years of age and freeof any neurologic problems.

Patients were excluded if they were noted to have a history ofstrabismus, diplopia, palsy of CN III, IV or VI, papilledema, opticneuropathy or other known disorder of CN II, macular edema, retinaldegeneration, dementia or cognitive impairment, sarcoidosis, myastheniagravis, multiple sclerosis or other demyelinating disease. Comatose andsedated individuals were excluded.

Pregnant individuals, prisoners, subjects who were missing eyes, notopening eyes, or wearing excessive mascara/false eyelashes were excludedfrom the study.

All trauma patients were recruited from a concussion referral center andwere subject to same inclusion and exclusion criteria as controls exceptfor recent head injury, consentable and able/willing to participate inthe study.

All children underwent eye tracking as well as two tests forconvergence:

Convergence blurry—is the point at which an object moving closer to thenose becomes blurry.

Convergence double—is the point at which an object moving closer to thenose becomes double.

Definition of concussion: For the purposes of assessing eye movement asa biomarker for concussion, we defined concussion according to CDC acuteconcussion evaluation tool¹ using the symptom checklist developed byLovell and Collins.² Precisely, patients were labelled as concussed ifthere was a positive injury description with evidence of forcibledirect/indirect blow to the head, plus evidence of active symptoms ofany type and number related to the trauma (Total Symptom Score>0), withor without evidence of loss of consciousness (LOC), skull fracture orintracranial injury.

Eye tracking procedure: Subjects' eye movements were recorded with an SRResearch Eyelink 1000 eye tracker while a 220-second video was playedcontinuously within a square aperture moving around the perimeter of a17″ viewing monitor (aspect ratio 4:3) fixed 55 cm away from thepatient. The video aperture size was approximately 1/9^(th) the area ofthe display monitor. The position of the eyes were obtained at 500 Hzwith a stabilized chin rest to minimize head movement during the eyetracking session. All subjects were asked to take off their glasses whenbeing tracked. The visual stimuli were Disney PG music videos (e.g. LionKing, Hercules, and Puss in Boots). The total visible span of the movingaperture was somewhat approximately 17° horizontally and 13° verticallyfrom mid-position with a caveat that the subject may be viewingdifferent portions of the aperture during each cycle. The first and last10 seconds of each data set were discarded to yield 200 seconds of data,yielding 100,000 data points. Both, the afferent stimulus presentationand eye tracking was binocular. Subjects were not spatially calibratedto the tracker to enable independent analysis of each pupil positionover time.

The eye tracking data was processed to yield 89 eye tracking metrics asdiscussed previously.³

Statistical analysis: Statistical analyses were carried out usingStatistical Package for the Social Sciences (SPSS version 19, IBMCorporation, Armonk, N.Y.).

Selection of candidate eye tracking metrics: An age and gender balancedsample was drawn from the pool of cases and controls to build model.Descriptive statistics were calculated for age and gender. Eye trackingmetrics were compared using Wilcoxon rank sum tests to identify themetrics that correlated with concussion. Since an ideal biomarker shouldbe independent of age and gender, these metrics were tested usingWilcoxon rank sum tests to identify their correlation with gender andwith spearman correlation to identify their association with age.

Development and validation of a predictive model for concussion: We usedlogistic regression to build a model correlating eyetracking metrics topresence or absence of concussion. A receiver operating curve analysiswas then carried out for this model, and an optimal cut off wasdetermined. The frequencies of true positive, true negative, falsepositive and false negative were then calculated to appraise the modelaccuracy. The model was externally validated in the subjects not used inbuilding the model.

Development and validation of a predictive model for near point ofconvergence: In addition to serve as a diagnostic tool concussion, wealso tested whether or not eye tracking correlates with abnormality inthe near point of convergence, a common clinical diagnosis of vision inconcussed population, used in athlete sideline assessment tools.⁴⁵ Weidentified candidate eye tracking metrics that significantly correlatedwith abnormality in near point of convergence (NPC>6 cm) using Wilcoxonrank sum tests. The parameters thus identified were used to build amodel to predict the probability of having an abnormal near point ofconvergence. A receiver operating curve analysis was constructed toappraise model accuracy.

Results

A total of 56 pediatric patients with concussion and 83 pediatriccontrols underwent eye tracking prospectively. The cases were on average22.4 weeks (range: 0-109 weeks) out from injury.

The symptom severity score is ranked out from 1 to 22. The data rangedfrom 1-17 indicating the most severe score was never reached in thispopulation. FIG. 7 displays the prevalence of different symptoms, whileFIG. 8 displays the prevalence of different signs in the concussedgroup. Headache was the most common symptom and was present in 76.8%(43) of the cases, 58.9% had dizziness, 14.3% (8) cases failed to tracka fast moving object, and in 57.1% tracking provoked symptoms. 85.7%cases had symptoms with horizontal saccades, and 78.6% had symptoms withvertical saccades.

To balance age and gender in both groups, a balanced sample of 32 casesand 32 controls was obtained to create model. The descriptive statisticsfor balanced sample are given in Table 1 for age and in Table 2 forgender. The groups didn't differ in ages (p-value=0.979). Eye trackingmetrics that significantly correlated with concussion are listed inTable 3. These metrics didn't correlate significantly with gender, orhad a strong association with age.

We then built a logistic regression model that correlated eye trackingmetrics to the state of being concussed. The metrics in this model arelisted in Table 4 (FIG. 5). Interestingly, the model included themeasures from both eyes and two measures of conjugacy.

We then performed receiver operating characteristic (ROC) curve analysisfor the probability of being concussed predicted by the model built tothe state of being concussed according to CDC criteria. The area underthe receiver operating curve was 87.2% with a 95% confidence interval of81.6%-92.7%. FIG. 1 displays an ROC. The cut-off point selected by themaximum of the Youden Index (sensitivity+specificity=1), with the modelhaving an accuracy of 80.6% with a sensitivity of 80.8% and specificityof 80.4% at this cut-off. The frequencies of true and false positivesand negatives are given in Table 5.

The model was validated in population of 24 cases and 51 controls, thedescriptive statistics of which listed in Table 6 for age and in Table 7(FIG. 6) for gender. The receiver operating curve analysis yielded anarea under the curve of 0.789 in this external population. FIG. 11.Table 8 displays the frequencies of true and false positives andnegatives in external population.

Correlation of eye tracking with abnormality in near point ofconvergence: We also tested whether eye tracking correlates with nearpoint of convergence and found right_velBot_value and conj_velRit_valueto be a significant predictor of abnormal near point of convergence. Amodel built using these parameters to classify the cases based on theirnear point of convergence status achieved a specificity of 95.8% and asensitivity of 57.1%. A receiver operating curve analysis indicated anarea under the curve of 0.81. See FIG. 13.

Similarity in adult and pediatric eye tracking metrics: We compared theeye tracking metrics to previously published eye tracking metrics inadult population¹ and found six eye tracking metrics(conj_varXbot_value, left_distBot_value, left_distLef_value,left_varYtop_value, right_distBot_value, right_distRit_value) that weresignificant for both adult and pediatric population. This indicates thateye tracking metrics related concussion are strongly conserved in adultER and pediatric concussion referral center populations.

TABLE 1 Descriptive statistics for age. Statistics Age Gender SymptomSymptom .00 1.00 .00 1.00 N Valid 32 32 32 32 Missing 0 0 0 0 Mean13.406250 13.437500 .5313 .5313 Std. Error of Mean .8482185 .8363437.08963 .08963 Median 13.500000 13.500000 1.0000 1.0000 Mode 11.000012.0000 1.00 1.00 Std. Deviation 4.7982482 4.7310744 .50701 .50701Variance 23.023 22.383 .257 .257 Skewness −.275 −.345 −.131 −.131 Std.Error of .414 .414 .414 .414 Skewness Kurtosis −.831 −.615 −2.119 −2.119Std. Error of .809 .809 .809 .809 Kurtosis Range 17.0000 17.0000 1.001.00 Minimum 4.0000 4.0000 .00 .00 Maximum 21.0000 21.0000 1.00 1.00 Sum429.0000 430.0000 17.00 17.00 Percentiles 10 5.600000 6.300000 .0000.0000 20 8.600000 9.000000 .0000 .0000 25 11.000000 11.000000 .0000.0000 30 11.000000 11.000000 .0000 .0000 40 11.200000 12.000000 .0000.0000 50 13.500000 13.500000 1.0000 1.0000 60 16.000000 15.800000 1.00001.0000 70 17.000000 17.000000 1.0000 1.0000 75 17.750000 17.0000001.0000 1.0000 80 18.000000 18.000000 1.0000 1.0000 90 19.70000019.700000 1.0000 1.0000

TABLE 2 Frequencies of age: Age Cumulative Frequency Percent ValidPercent Percent Symptom Symptom Symptom Symptom .00 1.00 .00 1.00 .001.00 .00 1.00 Valid 4.0000 1 2 3.1 6.3 3.1 6.3 3.1 6.3 5.0000 2 6.3 6.39.4 6.0000 1 3.1 3.1 9.4 7.0000 1 2 3.1 6.3 3.1 6.3 12.5 15.6 8.0000 26.3 6.3 18.8 9.0000 1 2 3.1 6.3 3.1 6.3 21.9 21.9 11.0000 6 3 18.8 9.418.8 9.4 40.6 31.3 12.0000 1 4 3.1 12.5 3.1 12.5 43.8 43.8 13.0000 2 26.3 6.3 6.3 6.3 50.0 50.0 14.0000 2 2 6.3 6.3 6.3 6.3 56.3 56.3 15.00001 3.1 3.1 59.4 16.0000 4 3 12.5 9.4 12.5 9.4 68.8 68.8 17.0000 2 3 6.39.4 6.3 9.4 75.0 78.1 18.0000 4 3 12.5 9.4 12.5 9.4 87.5 87.5 19.0000 11 3.1 3.1 3.1 3.1 90.6 90.6 20.0000 1 1 3.1 3.1 3.1 3.1 93.8 93.821.0000 2 2 6.3 6.3 6.3 6.3 100.0 100.0 Total 32 32 100.0 100.0 100.0100.0

TABLE 3 Frequencies of Gender Gender Cumulative Frequency Percent ValidPercent Percent Symptom Symptom Symptom Symptom .00 1.00 .00 1.00 .001.00 .00 1.00 Valid Female 15 15 46.9 46.9 46.9 46.9 46.9 46.9 Male 1717 53.1 53.1 53.1 53.1 100.0 100.0 Total 32 32 100.0 100.0 100.0 100.0

TABLE 5 The Mann-Whitney U test was used to identify eye trackingmetrics that were significantly associated with concussion Mann- Asymp.Sig. Test Statistics Whitney U Wilcoxon W Z (2-tailed)left#distBot#value 326 854 −2.49745 0.012508988 left#distLef#value 327855 −2.48402 0.012990725 right#distRit#value 328 856 −2.4706 0.013488799right#distBot#value 331 859 −2.43031 0.015085711 right#skewRitNorm#value338 866 −2.33632 0.019474321 left#skewRit#value 340 868 −2.309470.020917489 right#skewRit#value 342 870 −2.28262 0.022452988conj#varXbot#value 345 873 −2.24233 0.024939749 left#skewRitNorm#value347 875 −2.21548 0.026727109 conj#varXtopbotRatio#value 348 876 −2.202050.027661548 conj#varYtopbotRatio#value 348 876 −2.20205 0.027661548left#varYtop#value 352 880 −2.14834 0.031686389

TABLE 6 Correlation of eye tracking metrics with age. None of the eyetracking metric had a strong correlation with age. Spearman's rho,Correlation Coefficient Age conj#boxscore#value −.389**right#velLef#value .304* right#varXrit#value −.291* conj#boxscore5#value−.280* right#velRit#value .280* left#varXrit#value −.268*right#heightmedian#value .262* right#widthmedian#value .262*conj#varAspect#value −.259* right#areamedian#value .259*right#velTop#value .259* right#heightmean#value .258*right#distRit#value −.257* conj#velTop#value −.255* Age 1.000

TABLE 8 Area Under the Curve Test Result Variable(s): Predictedprobability Asymptotic 95% Confidence Interval Area Std. Error^(a)Asymptotic Sig.^(b) Lower Bound Upper Bound. .854 .046 .000 .764 .943^(a)Under the nonparametric assumption ^(b)Null hypothesis: true area =0.5

TABLE 9 Eye tracking model for convergence blurry (the point at which anobject moving closer to the nose becomes blurry) Linear regression:Coefficients^(a) 95.0% Confidence Interval for B Model t Sig. LowerBound Upper Bound 7 (Constant) −1.160 .252 −11.722 3.159left#velLef#value −2.668 .011 −9.932 −1.383 conj#varX#value −.550 .585−86.849 49.615 left#distRit#value 4.989 .000 1948.045 4588.569right#areamedian#value 3.905 .000 1.807 5.661 conj#varYtopbotRatio#value2.496 .016 .018 .166 left#heightmean#value −2.033 .048 −8.728 −.037right#distLef#value −2.011 .051 −2028.974 2.422 ^(a)Dependent Variable:convergence blurry

Based on these metrics we generated an equation to predict the value ofCONVERGENCE_BLURRY.

The equation has a Pearson correlation of 0.781 (p-value<0.001) and aspearman correlation of 0.785 (p-value<0.001) with actualCONVERGENCE_BLURRY scores.

Correlation with break_double (the point at which an object movingcloser to the nose is seen as double):

The break_double variable is difficult to model, since there are a lotof 1s 2s and 3s, and it appears to be ordinal instead of linear, butsince break double has a strong correlation with CONVERGENCE_BLURRY, sowe can use exact same equation for break double too, but with a spearmancorrelation of 0.554.

TABLE 10 showing correlations of the convergence blurry and convergencedouble model with each other. Correlations convergence break Pred blurry(double) convergenceblurry Spearman's rho convergence blurry CorrelationCoefficient 1.000 .752** .815** Sig. (2-tailed) . .000 .000 N 55 54 55break (double) Correlation Coefficient .752** 1.000 .554** Sig.(2-tailed) .000 . .000 N 54 54 54 Pred_convergenceblurry CorrelationCoefficient .815** .554** 1.000 Sig. (2-tailed) .000 .000 . N 55 54 139**Correlation is significant at the 0.01 level (2-tailed).

The invention claimed is:
 1. A method for predicting abnormal eyeconvergence in a human or animal subject, the method comprising: usingan eye tracker having a camera to track pupil positions of a first eyeand a second eye of the subject to generate eye tracking data for thesubject as the subject watches a video, the eye tracking data includinga plurality of data points for each of the first eye and the second eye;temporally calibrating the eye tracker by independently predicting thepupil positions of the first eye and the second eye of the subject foreach of the plurality of data points based on time elapsed since a startof the video; calculating one or more metrics based on the eye trackingdata, the one or more metrics comprising at least conjugacy metrics ofboth eyes of the subject; determining a logistic regression model basedon the one or more metrics; and using at least the logistic regressionmodel to compare movement of the first eye to the second eye and predictwhether the subject has abnormal eye convergence.
 2. The method as inclaim 1, wherein generating the eye tracking data comprises: analyzingtracked eye movement; and comparing the tracked eye movement to a normalor mean eye movement.
 3. The method as in claim 2, wherein generatingthe eye tracking data further comprises, after the comparing of thetracked eye movement, calculating a standard deviation or p value forthe tracked eye movement as compared to the normal or mean eye movement.4. The method as in claim 2, wherein the comparing of the tracked eyemovement comprises comparing eye movement of said both eyes of thesubject to eye movement of one or both eyes of one or more othersubjects or controls.
 5. The method as in claim 1, further comprisingpredicting whether a brain injury has occurred in the subject, based onthe prediction of whether the subject has abnormal eye convergence. 6.The method as in claim 5, wherein predicting whether the brain injuryhas occurred comprises predicting whether a concussion has occurred byperforming a receiver operating curve analysis of the logisticregression model to determine a cut off, and wherein the logisticregression model correlates the one or more metrics with the concussionbased on the cut off.
 7. The method as in claim 1, wherein eye movementis tracked for at least 40 seconds.
 8. A system for predicting abnormaleye convergence in a human or animal subject, the system comprising: aneye tracking camera configured to track eye movement of the subjectwhile the subject watches a video; and a processor coupled with the eyetracker having a camera and containing program instructions that, whenexecuted, cause the processor to: process the tracked eye movement togenerate eye tracking data for a first eye and a second eye, the eyetracking data including a plurality of data points for each of the firsteye and the second eye; temporally calibrate the eye tracker byindependently predicting pupil positions of the first eye and the secondeye for each of the plurality of data points based on time elapsed sincea start of the video; calculate one or more metrics based on the eyetracking data, the one or more metrics comprising at least conjugacymetrics of both eyes of the subject; determine a logistic regressionmodel based on the at least one or more metrics; and use at least thelogistic regression model to compare movement of the first eye to thesecond eye and predict whether the subject has abnormal eye convergence.9. The system of claim 8, wherein the instructions for tracking the eyemovement of the subject comprises instructions configured to trackingmovement of said both eyes of the subject.
 10. The system of claim 8,wherein the instructions for generating the eye tracking data areconfigured to: analyze the tracked eye movement; and compare the trackedeye movement to a normal or mean eye movement.
 11. The system of claim10, wherein the instructions for generating the eye tracking data arefurther configured to, after the comparing, calculate a standarddeviation or p value for the tracked eye movement as compared to thenormal or mean eye movement.
 12. The system of claim 8, furthercomprising instructions for predicting whether a brain injury hasoccurred in the subject based on determining whether the subject hasabnormal eye convergence.
 13. System of claim 12, wherein theinstructions for predicting whether the brain injury has occurred areconfigured to determine whether a concussion has occurred by performinga receiver operating curve analysis of the logistic regression model todetermine a cut off, and wherein the logistic regression modelcorrelates the one or more metrics with the concussion based on the cutoff.
 14. A non-transitory computer-readable medium having instructionsstored thereon for predicting abnormal eye convergence in a human oranimal subject, the instructions configured to perform operationscomprising: receiving, via an eye tracker having a camera, eye movementdata pertaining to pupil positions of both eyes of the subject whilewatching a video, the eye tracking data including a plurality of datapoints for said both eyes; temporally calibrating the eye tracker byindependently predicting the pupil positions of said both eyes of thesubject for each of the plurality of data points based on time elapsedsince a start of the video; analyzing the eye movement data of said botheyes of the subject to determine one or more metrics, the one or moremetrics comprising at least conjugacy metrics of said both eyes of thesubject; determining a logistic regression model based on the one ormore metrics; and using at least the logistic regression model tocompare movement of the first eye to the second eye and predict whetherthe subject has abnormal eye convergence.
 15. A computer-readable mediumas in claim 14, wherein the instructions are further configured toperform calculating a standard deviation or p value for eye movement ofsaid both eyes of the subject as compared to a normal or mean eyemovement, before the determining of the one or more metrics.
 16. Acomputer-readable medium as in claim 14, wherein the instructions forreceiving the eye movement data are further configured to performoperations comprising: tracking eye movement of at least one eye of thesubject; collecting raw x and y cartesian coordinates of the pupilpositions; and normalizing the raw x and y cartesian coordinates.